{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3aedfc0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms as T\n",
    "from torchvision.datasets import ImageFolder\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from torch.utils.data import DataLoader\n",
    "from pytorch_grad_cam import GradCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, GuidedBackpropReLUModel\n",
    "from pytorch_grad_cam.utils.image import show_cam_on_image, deprocess_image\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "from train import create_net\n",
    "from utils.eval import eval_net\n",
    "from utils.datasets import AortaDataset3D\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "np.random.seed(63910)\n",
    "torch.manual_seed(53152)\n",
    "torch.cuda.manual_seed_all(7987)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "126d5809",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0,2'\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "#device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4475572",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = T.Compose([\n",
    "    T.Resize(51), # 缩放图片(Image)，保持长宽比不变，最短边为img_size像素\n",
    "    T.CenterCrop(51), # 从图片中间切出img_size*img_size的图片\n",
    "    T.ToTensor(), # 将图片(Image)转成Tensor，归一化至[0, 1]\n",
    "    #T.Normalize(mean=[.5], std=[.5]) # 标准化至[-1, 1]，规定均值和标准差\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93f03b96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b0efcfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "val = ImageFolder('/nfs3-p1/zsxm/dataset/aorta_classify_ct_-100_500/val/', transform=transform, loader=lambda path: Image.open(path))\n",
    "val_loader = DataLoader(val, batch_size=64, shuffle=True, num_workers=8, pin_memory=True, drop_last=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "858f4dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = create_net(device, 1, 2, 'checkpoints/09-28_16:53:49/Net_best.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a980f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_layer = net.layer4[-1]\n",
    "cam = GradCAM(model=net, target_layer=target_layer, use_cuda=True)\n",
    "gb_model = GuidedBackpropReLUModel(model=net, use_cuda=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fc9d9f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for imgs, true_categories in val_loader:\n",
    "    imgs = imgs.to(device=device, dtype=torch.float32)\n",
    "    true_categories = true_categories.to(device=device, dtype=torch.long)\n",
    "    print(true_categories.view(8, 8))\n",
    "    label_list = []\n",
    "    cam_image_list = []\n",
    "    gb_list = []\n",
    "    cam_gb_list = []\n",
    "    for i, img in enumerate(imgs):\n",
    "        grayscale_cam = cam(img.unsqueeze(0), target_category=true_categories[i].item(),aug_smooth=True, eigen_smooth=True)\n",
    "        if true_categories[i].item() == 0:\n",
    "            label_list.append(torch.zeros(3,51,51,dtype=torch.uint8))\n",
    "        elif true_categories[i].item() == 1:\n",
    "            label_list.append(torch.ones(3,51,51,dtype=torch.uint8)*255)\n",
    "        grayscale_cam = grayscale_cam[0, :]\n",
    "        cam_image = show_cam_on_image(imgs[i].permute(1,2,0).cpu().numpy(), grayscale_cam, use_rgb=True)\n",
    "        cam_image_list.append(torch.from_numpy(cam_image).permute(2,0,1))\n",
    "        cam_image = cv2.cvtColor(cam_image, cv2.COLOR_RGB2BGR)\n",
    "        gb = gb_model(img.unsqueeze(0), target_category=true_categories[i].item())\n",
    "        cam_mask = cv2.merge([grayscale_cam, grayscale_cam, grayscale_cam])\n",
    "        cam_gb = cv2.cvtColor(deprocess_image(cam_mask * gb), cv2.COLOR_BGR2RGB)\n",
    "        gb = cv2.cvtColor(deprocess_image(gb), cv2.COLOR_BGR2RGB)\n",
    "        gb_list.append(torch.from_numpy(gb).permute(2,0,1))\n",
    "        cam_gb_list.append(torch.from_numpy(cam_gb).permute(2,0,1))\n",
    "    display(Image.fromarray((torchvision.utils.make_grid(imgs).permute(1,2,0).cpu().numpy()*255).astype(np.uint8)))\n",
    "    display(Image.fromarray(torchvision.utils.make_grid(torch.stack(label_list)).permute(1,2,0).numpy()))\n",
    "    display(Image.fromarray(torchvision.utils.make_grid(torch.stack(cam_image_list)).permute(1,2,0).numpy()))\n",
    "    display(Image.fromarray(torchvision.utils.make_grid(torch.stack(gb_list)).permute(1,2,0).numpy()))\n",
    "    display(Image.fromarray(torchvision.utils.make_grid(torch.stack(cam_gb_list)).permute(1,2,0).numpy()))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aad92632",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "097a5e18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for imgs, true_categories in val_loader:\n",
    "    imgs = imgs.to(device=device, dtype=torch.float32)\n",
    "    true_categories = true_categories.to(device=device, dtype=torch.long)\n",
    "    print(true_categories.view(8, 8))\n",
    "    grayscale_cams = cam(input_tensor=imgs, target_category=true_categories, aug_smooth=True, eigen_smooth=True)\n",
    "    visualization_list = []\n",
    "    for i, grayscale_cam in enumerate(grayscale_cams):\n",
    "        visualization = torch.from_numpy(show_cam_on_image(imgs[i].permute(1,2,0).cpu().numpy(), grayscale_cam)).permute(2,0,1)\n",
    "        visualization_list.append(visualization)\n",
    "    display(Image.fromarray(torchvision.utils.make_grid(torch.stack(visualization_list)).permute(1,2,0).numpy()))\n",
    "    display(Image.fromarray((torchvision.utils.make_grid(imgs).permute(1,2,0).cpu().numpy()*255).astype(np.uint8)))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9be9ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for imgs, true_categories in val_loader:\n",
    "    imgs = imgs.to(device=device, dtype=torch.float32)\n",
    "    true_categories = true_categories.to(device=device, dtype=torch.long)\n",
    "    print(true_categories.view(16, 8))\n",
    "    cam_image_list = []\n",
    "    for i, img in enumerate(imgs):\n",
    "        grayscale_cam = cam(input_img=img.unsqueeze(0), target_category=true_categories[i].item())\n",
    "        cam_img = torch.from_numpy(show_cam_on_image(imgs[i].permute(1,2,0).cpu().numpy(), grayscale_cam)).permute(2,0,1)\n",
    "        visualization_list.append(visualization)\n",
    "    display(Image.fromarray(torchvision.utils.make_grid(torch.stack(visualization_list)).permute(1,2,0).numpy()))\n",
    "    display(Image.fromarray((torchvision.utils.make_grid(imgs).permute(1,2,0).cpu().numpy()*255).astype(np.uint8)))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c97a817c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df906e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "ori_img = Image.open('/nfs3-p1/zsxm/dataset/aorta_classify_ct_-100_500/1/liuhongfu-J-57-200_j_0089.png')\n",
    "tensor_img = transform(ori_img).unsqueeze(0)\n",
    "tensor_label = torch.tensor([[1]], dtype=torch.long)\n",
    "print(tensor_img.shape, tensor_label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32e7d4a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "grayscale_cam = cam(input_img=tensor_img, target_category=1)\n",
    "grayscale_cam = grayscale_cam[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a61621c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "visualization = show_cam_on_image(tensor_img.squeeze(0).permute(1,2,0).numpy(), grayscale_cam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09c1eb3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "display(Image.fromarray(visualization))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bff699c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ffbc7ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5fa9d09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c60c3fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "val = AortaDataset3D('/nfs3-p1/zsxm/dataset/aorta_classify_ct_-100_500/val/', depth=7, transform=transform, )\n",
    "val_loader = DataLoader(val, batch_size=64, shuffle=True, num_workers=8, pin_memory=True, drop_last=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fb57b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = create_net(device, load_model='checkpoints/09-30_15:59:58/Net_best.pth', flag_3d=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9125e33c",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_score, val_loss, PR_curve_img = eval_net(net, val_loader, device, final=True, PR_curve_save_dir='checkpoints/09-30_15:59:58/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0ba37a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(val_score, val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2219b19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open('111.png')\n",
    "img = transform(img).unsqueeze(0).to(device=device)\n",
    "img.requires_grad_()\n",
    "net.zero_grad()\n",
    "pred_cate = net(img)\n",
    "print(f'pred_cate:{pred_cate.item()}')\n",
    "target = torch.tensor([[0]], dtype=torch.float32).to(device=device, dtype=torch.float32)\n",
    "loss = F.binary_cross_entropy_with_logits(pred_cate, target)\n",
    "print(f'loss:{loss.item()}')\n",
    "loss.backward()\n",
    "grad = img.grad.data.squeeze(0).permute(1, 2, 0).squeeze(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a7a4862",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "img_grad = torch.abs(grad)\n",
    "img_grad = (img_grad / img_grad.max() * 255).type(torch.uint8)\n",
    "img_grad = Image.fromarray(img_grad.cpu().numpy())\n",
    "display(img_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc98ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path='/nfs3-p1/zsxm/dataset/aorta_classify/0/'\n",
    "error_0_list = []\n",
    "for f in os.listdir(input_path):\n",
    "    img = Image.open(os.path.join(input_path, f))\n",
    "    img = transform(img).unsqueeze(0).to(device=device)\n",
    "    with torch.no_grad():\n",
    "        pred_cate = net(img)\n",
    "        if pred_cate.item() > 0:\n",
    "            print(f)\n",
    "            error_0_list.append(f)\n",
    "            \n",
    "print(f'len of error 0:{len(error_0_list)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb7e19fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path='/nfs3-p1/zsxm/dataset/aorta_classify/1/'\n",
    "error_1_list = []\n",
    "for f in os.listdir(input_path):\n",
    "    img = Image.open(os.path.join(input_path, f))\n",
    "    img = transform(img).unsqueeze(0).to(device=device)\n",
    "    with torch.no_grad():\n",
    "        pred_cate = net(img)\n",
    "        if pred_cate.item() < 0:\n",
    "            print(f)\n",
    "            error_1_list.append(f)\n",
    "            \n",
    "print(f'len of error 1:{len(error_1_list)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14661bf1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c8dff45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de28878b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "491e2c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: **********************************************************************\n",
      "Network: ResNet\n",
      "\t1 input channels\n",
      "\t1 output channels (classes)\n",
      "\t3D model: False\n",
      "\n",
      "[INFO]: Model loaded from checkpoints/10-18_21:22:03/Net_best.pth\n",
      "[INFO]: torch.cuda.device_count:2, Use nn.DataParallel\n"
     ]
    }
   ],
   "source": [
    "net = create_net(device, 1, 1, 'checkpoints/10-18_21:22:03/Net_best.pth')\n",
    "net.eval()\n",
    "\n",
    "pos_pos_list = []\n",
    "pos_neg_list = []\n",
    "neg_pos_list = []\n",
    "neg_neg_list = []\n",
    "\n",
    "def get_pos_vector(module, input, output):\n",
    "    res = output[0].item()\n",
    "    if res > 0:\n",
    "        pos_pos_list.append(input[0].squeeze(0).tolist())\n",
    "    else:\n",
    "        pos_neg_list.append(input[0].squeeze(0).tolist())\n",
    "    \n",
    "handle = net.module.fc.register_forward_hook(get_pos_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3e339833",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_path = '/nfs3-p2/zsxm/dataset/aorta_classify_ct_-100_500/val/1'\n",
    "for img_path in os.listdir(pos_path):\n",
    "    img = Image.open(os.path.join(pos_path, img_path))\n",
    "    img = transform(img).unsqueeze(0)\n",
    "    res = torch.sigmoid(net(img)).item()\n",
    "#     if res > 0.5:\n",
    "#         shutil.copy(os.path.join(pos_path, img_path), '/nfs3-p2/zsxm/dataset/aorta_classify_ct_-100_500/val/t1-1')\n",
    "#     else:\n",
    "#         shutil.copy(os.path.join(pos_path, img_path), '/nfs3-p2/zsxm/dataset/aorta_classify_ct_-100_500/val/t1-0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "af8f9116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3482\n",
      "864\n"
     ]
    }
   ],
   "source": [
    "print(len(pos_pos_list))\n",
    "print(len(pos_neg_list))\n",
    "handle.remove()\n",
    "def get_neg_vector(module, input, output):\n",
    "    res = output[0].item()\n",
    "    if res > 0:\n",
    "        neg_pos_list.append(input[0].squeeze(0).tolist())\n",
    "    else:\n",
    "        neg_neg_list.append(input[0].squeeze(0).tolist())\n",
    "    \n",
    "handle = net.module.fc.register_forward_hook(get_neg_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6a07f493",
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_path = '/nfs3-p2/zsxm/dataset/aorta_classify_ct_-100_500/val/0'\n",
    "for img_path in os.listdir(neg_path):\n",
    "    img = Image.open(os.path.join(neg_path, img_path))\n",
    "    img = transform(img).unsqueeze(0)\n",
    "    res = torch.sigmoid(net(img)).item()\n",
    "#     if res > 0.5:\n",
    "#         shutil.copy(os.path.join(neg_path, img_path), '/nfs3-p2/zsxm/dataset/aorta_classify_ct_-100_500/val/t0-1')\n",
    "#     else:\n",
    "#         shutil.copy(os.path.join(neg_path, img_path), '/nfs3-p2/zsxm/dataset/aorta_classify_ct_-100_500/val/t0-0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fd1dab82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "633\n",
      "7835\n"
     ]
    }
   ],
   "source": [
    "print(len(neg_pos_list))\n",
    "print(len(neg_neg_list))\n",
    "handle.remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d1a3b566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4346\n",
      "8468\n",
      "12814\n"
     ]
    }
   ],
   "source": [
    "pos_list = pos_pos_list + pos_neg_list\n",
    "neg_list = neg_pos_list + neg_neg_list\n",
    "tot_list = pos_list + neg_list\n",
    "print(len(pos_list))\n",
    "print(len(neg_list))\n",
    "print(len(tot_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "84836948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(np.array(tot_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e5dd9773",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_new = pca.transform(np.array(pos_list))\n",
    "neg_new = pca.transform(np.array(neg_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e549bf79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pos_new[:, 0], pos_new[:, 1], color='r', label='positive')\n",
    "plt.scatter(neg_new[:, 0], neg_new[:, 1], color='b', label='negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "225eacf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pos_new[:, 0], pos_new[:, 1], color='r', label='positive')\n",
    "#plt.scatter(neg_new[:, 0], neg_new[:, 1], color='b', label='negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "13499732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(pos_new[:, 0], pos_new[:, 1], color='r', label='positive')\n",
    "plt.scatter(neg_new[:, 0], neg_new[:, 1], color='b', label='negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "54960279",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_pos_new = pca.transform(np.array(pos_pos_list))\n",
    "pos_neg_new = pca.transform(np.array(pos_neg_list))\n",
    "neg_pos_new = pca.transform(np.array(neg_pos_list))\n",
    "neg_neg_new = pca.transform(np.array(neg_neg_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "c11fd3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pos_pos_new[:, 0], pos_pos_new[:, 1], color='r', label='true positive')\n",
    "plt.scatter(pos_neg_new[:, 0], pos_neg_new[:, 1], color='g', label='false negative')\n",
    "plt.scatter(neg_neg_new[:, 0], neg_neg_new[:, 1], color='b', label='true negative')\n",
    "plt.scatter(neg_pos_new[:, 0], neg_pos_new[:, 1], color='y', label='false negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "bc19e474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pos_pos_new[:, 0], pos_pos_new[:, 1], color='r', label='true positive')\n",
    "# plt.scatter(pos_neg_new[:, 0], pos_neg_new[:, 1], color='g', label='false negative')\n",
    "# plt.scatter(neg_neg_new[:, 0], neg_neg_new[:, 1], color='b', label='true negative')\n",
    "# plt.scatter(neg_pos_new[:, 0], neg_pos_new[:, 1], color='y', label='false negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2770aeb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(pos_pos_new[:, 0], pos_pos_new[:, 1], color='r', label='true positive')\n",
    "plt.scatter(pos_neg_new[:, 0], pos_neg_new[:, 1], color='g', label='false negative')\n",
    "# plt.scatter(neg_neg_new[:, 0], neg_neg_new[:, 1], color='b', label='true negative')\n",
    "# plt.scatter(neg_pos_new[:, 0], neg_pos_new[:, 1], color='y', label='false negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6e8d16d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(pos_pos_new[:, 0], pos_pos_new[:, 1], color='r', label='true positive')\n",
    "# plt.scatter(pos_neg_new[:, 0], pos_neg_new[:, 1], color='g', label='false negative')\n",
    "plt.scatter(neg_neg_new[:, 0], neg_neg_new[:, 1], color='b', label='true negative')\n",
    "# plt.scatter(neg_pos_new[:, 0], neg_pos_new[:, 1], color='y', label='false negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "ae1e4501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(pos_pos_new[:, 0], pos_pos_new[:, 1], color='r', label='true positive')\n",
    "# plt.scatter(pos_neg_new[:, 0], pos_neg_new[:, 1], color='g', label='false negative')\n",
    "# plt.scatter(neg_neg_new[:, 0], neg_neg_new[:, 1], color='b', label='true negative')\n",
    "plt.scatter(neg_pos_new[:, 0], neg_pos_new[:, 1], color='y', label='false negative')\n",
    "plt.xlim(-15, 110)\n",
    "plt.ylim(-30,30)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "0e34e6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(n_components=2, learning_rate='auto', init='random', n_iter=1000, perplexity=30)\n",
    "tot_new = tsne.fit_transform(np.array(tot_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "83ac7b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tot_new[:4346, 0], tot_new[:4346, 1], color='r', label='positive')\n",
    "plt.scatter(tot_new[4346:, 0], tot_new[4346:, 1], color='b', label='negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "a6164c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZW9A5ftN+kyrA14CrgLuBT7by2UKndxgc9J8+Xq22vz5u0D5F4puSHnSMwcFkP1tkpqZUe3uzT03gl6agvz97W4pSKpXg98bHs/6VtQQB6R1Oq0iqAKuAI8DZrvgfAp4E7gPOCfjMGDANTK9cuTLZFkqSVhfNrlTCBXxqKni/cQls0P5Fwj/bTQu/t5LLaMmS7IXLr7STZ6dazd7uLEr977sL8lhlKv7AEmA38EH39ZuBCo7baRK4L2wfXdnzb1aiCGWUH28Sdke9uHTBH6flhHdZC5eVzkt/v3M+x8dPfy9KxyxnZCb+QB/wDeA3At5fBewN20+hxT9IQMJ6VmEim7RrpZt67+3SShsPDGQvXFbiKUNDzd8vUGcmSPyTjvYR4E+B/ar6ubr6FXWbXQ/sTdKOzAmaPXvPPf7r43qEDd4mnZLBZv1GG7iu1eCss5yp/0Z38NRTzd/vhrWr/a4IcRXgFwDF8e3vccs1wAPAd936rwMrwvZV6J5/M6amggeXogzedoNrJc+E9fxtILXcdzwFCGAg6wHfTkvXir9qOoO3RnuMj59+bupdX+2M5zSWajWe/WRRqtXmv9+ylEolt1FAQeJvc7/zwOio8xPyowxx8XmlVoPt2xeeGxHYsGHe9dXp+fHmARw/Dn19ne0rbUQc12Wz329ZmJtzEr55czuWLcu9S8jEPy8E5X2xfPrZ4TdLWRUeq5uTuHRpZ8fwRHNmBt54o7N9pc3GjfMXwbC8RWVjZsZJ6ZHjC4CJf1K0OnPX8unnj2azqMucqE7EmeG6efN8nd/vt+y8/nq+05n4+YLyWArl8283RNIGb/NFmB++8RyXoTSLc/eLi7eS6k/WD8znnyLtJjUbHXUWSz91ynksU0hlHgnrzZZxScOxMf/fpTc+YpxOThd+MfFPgmax4ZbjvjjUz3MwHB4LyMHo1+ExHLZuzdoCX0z8kyBokHbp0vazZBaBbrqwed/l5pudSBzDoczZWttlbi5rC3wx8U+CoMFb6N4c952kf84bExOwfv38d7HlF+cJ6thYVFowlUrWFvhi4p8EQWkRjh3z374bek3dsHjLxITzR92yJWtL8kmz6LNui0rr749vX94KdjnDFnBPk2XL/HuRg4POAG+R6elxesmNFGWh64mJ+ERfxGmPnN7ut8XgoCPwzYIQliyx/EaNNIbEZoAt4J41tRr8+Men1/f3J9NriuJ/j9NHX3R3QJyDcg88UCzhj7JwfJTosy9+MRZzuoapKbjyyvyOg/nFf+axFCrO349mq3nFTZR5BnGnay56+udW4rabpeIWyT7R2+BgeEripGLVgxLdlXVhGL/fR8o5gLDEbhnTyapYrRIlB30SawEUeZJas2X7/IQwaEJTkit5LVoUvo13/uJOFBcHRU1eF3eJc6nVCASJv7l90iJNt0iUcLwkQvaKPEkt6qBcteo8bt7s+HO9SI5KxXndjs+7J+Lf8LXXwrd561sd90LYWhBZYCkgHFRzEQhh4p8WaebuiXKhKbqPPm4axdyP3l4ni2X9Z2ZnnT/z7Kzzup1Eb6dOOf7hVoXRz1e/a1f8wj8yEs9+/KLgpqac9hsaiucYRSEPEX5+twN5LIV3+6im5xbJwuffbUxNLfRTe3nrg7bt1KVRvx+R1txQSZahoWTb2W+9hDKUFNfpwHz+JcPvQtNYNz5eXB991tQLfhzi5bffLEsaC5WXORHcyEiybVtHkPhbnH9Z8Gbg1k/EWry4fGvyxoFfW3ZCteq4k+68s7XZxCKOlCRBGvMzenuLFRIbJ5WK4ypMgdzF+YvI1SJyQEQOishdWdlRGrphBm5eiDOJWU8P3HCDczFpNY1EJ8K/eLEzxhEU45/G2E9ZhR9y8d0zEX8RqQD/HXgfMAR8RERKNuKTMpaQKx5qtXgHVE+dciaFpZkR00s3snmzsxpX4wUgrUWEcprzJhVy8N2z6vlfARxU1WdU9XXgIeC6jGwpBxbd0zmeuydu0sga6oWiqi4Mw9282bn4NOahSsMVmNOcN6mQg++elfifB/xL3eujbt0CRGRMRKZFZPqHP/xhasZ1JbZMZOdknbM+6nyAekZGHMH3QlH9yGp+hhde2873KireRTjjfD+Qnfj7ORpPc2Cq6lZVHVbV4eXLl6dgVhcTlGnUBnujk7aLrFp1hMI7Z63OA+jrg1tvTca2OKjV4OGHi5H4rxOq1fn5DM0uwimTlfgfBS6oe30+8FxGthSHThOxFXkGbhLUak6mVRGnVCrOY1DbpuUi88TinnucpRG9dQVadQ+98UZ+B/RrNbjttu5dK6Fe8F96KZ//Nb/4z6QL0As8A6wG+oHvAJc2+0zp4/xtUla8TE2p9vUFx2H7ta3fOejvD47zr1Tay/UTd36ePJKHuQxxlxQnbrUCecrto6qzwCeAbwD7gYdVdV8WthSGZqGa3bR8Ylps2uT0jIM4cQI2bHDadNkyp9x8M5x5ptOr81xn990XvI+5OTh50olnb4XDh2Hdunzm54mLbosyK+D4mU3yKgpBi6WA88OzyVut0aw9w/DaF5yLSJhIJzkZKwp5/I/nNflcO0RZ6CZDcjfJq3R02jsP8jdXKjZ5qx068d+fOOHMxvXWLA4ja/Ht7XUuQL29zopleWByMt6lEtOk0eFT0PEzE/80iGNx86BQzaCZgt12Wx03k5NONEy7zMxkE/Y5MND6Z7zfyNycs1TlmWdm7xocHW3uMsszWbddTJj4p0EcqRWCQjUHB/23TysypajjDaOjcP/98/n5i8KiRZ3v47XXnEibrM9VAXvLgDMruhvwGwXOYylktE9YhsY4VvHKMgooKPqlWs0mU2inKbOjRqD09an29GQfXdIN0SlZt0G7JeWlGDsBS+mcMmGhhHH++bJaPjGKWGZ5IWr12EEphusvaNVqe2v0huXnz+JiksQSoq2Sl3UL2jmfBcHEP23CFqzuhhj9qHns0+hhxrEmcSdrH4eVZouWZLWYSR56/kXO6V8QgsTffP5J0WzmYrekVog6rpDG4HMcWUvD9tFuRk8vl4tfBs2swkD7+9ONS5+Y8I86aifVwchI9lkxg1JhFwm/K0IeS+F6/lF7DFm5bOLAz9WSVA+zvp2q1YXjCuPjwe6DuHr+Ub9rY/GbJVx/vpPunfb3O+0TdUnKuJmaCj43nt+8nR531u6inp7C/Fcxt0/KBLl9qtX5bdIcrI1ykWnnQtQoyo3jHHF8n3aFt15gouDnmvHsb0eo6891EGmIWF9fNkI1NRVum2ry3z+pEuX85gAT/7SZmjp9YLC/f+GfMA4/dVRb0lrQPa47mbgWM4/aln7fX2T+4tHMLz8+fvpFr15wmy0GH2Y7zH//TgaFsxCqKHY13pUUrRQAE/8sCBPCZgOAcdLJQKa3TTO3Sxy9ysb9txNR00lbhn3/oPcrleZtEhT15XUEwlxNYRFjrZS0e/9ZC7OJv6qqiX8uabfn32rvOspFptk2YW6XTl07nbh1wkrUnn/Y94/SO62/U/Bo5i4KGkvw2jPuHnHa0T1ZC3PSxdw+Jv5t046rpZ3PdNrzj+Lv7kRYkhr4bMXXHSS0AwP+F6awi4VHWBhns7uGuNsj7bj+rMU56WIDvib+HdFqL76du4VOff5R4tA7EZaoce5BLpaBAf/tW+mZBYl/O372duYFLF7s3DUkGQlUlJ5/VvMeWik2w9fEP1WaRU+EiW+70T7NQvUahbbdgd5OZwp3MnbSbiRPmHjV7z+q3z5p0Uu7p9rJBK64xnziLmmGycaEiX/RCfOLJ9Gri+qL7+/vLMTT7zh9fdEHlTsZO2l3rMGzze+9SuX0u6qsI1pGRqKdi7ipn4NRqaieeWa27dBJKVBvvx4T/6LTrHca59yAKCGWnuvFE+YgYWvlgtRJiGi7Yart9vi9fTdL2eDnVms3dLXTvD9ZCX8Q554bjxinWfKQB6lNTPyLTjOXQJzCH6Un3PhHSCtktd5OP/dUqxePdoWgvgfYzC1WHyabVDSTV4LWCs6b8KsWU/wh61Zrm9TFH/gj4HvAk8CjwJvc+lXAq8Aet9wbZX+lF/80JoRF7Qk3HjOtyWqqwULaji+208lj3vHCIn+SztjptXOjiyWPboqiCr+Jf0vi/16g133+aeDT7vNVwN5W91d68U8jFUSUAUe/Y6aZpqJd95ffnUGnYhCW+iHOyWrNSlEGIIucwdPEv+0LwfVAzX1u4t8urbo24goj9UqzfaSVoC7sAuV3txF0cYpjELbZRK20BnmLIv5ZJ2Pr9DwXlKzF/38C693nq4BXgH8C/gF4Z5PPjQHTwPTKlSsTbaCuo1lvPEiom8X0J/Xjj/sC5TfOEPSZOEIrveP5fY+o++/p6SzctCjClLWAd1KKcoH1IRHxB3YCe33KdXXbbHJ9/uK+PgOous8vB/4FODvsWNbzj0CUiJJqtbmLphVRjcPeVtxFUUImPeFNK21ys4lkUXv+nm++3Vw+eY5EaRYRVZSSx7GTFsik5w9sAL4FLG6yzd8Dw2H7MvEPodOIkrAEZkn0Lpv5ytv5ft4sWb/snO22S5T0DEET5sL8/X6Dsu2EhOa15190H38BJ3T5kcWA79XAU8DyhvrlQMV9fiHwLLA0bH8m/iF02sOtd1+kNXjbSvhqWFbN+h5/OyLeSalvn6gzhqOsARv1gpdXgSqij98vOV/ByUL8D7ounQUhncCvAPuA7wDfBn45yv5M/EOIGqnTbJEZT7TqQxOj9n7aGfANy3hZT7PvFbUdwnrUnYRkevts5SIThfqLSX0Yp/d98ir8qskJdBJl0aKsWysxbJJXtxMk6o09Y7/epF96hvoLRliWUb9jR+mRtpKrKEiwG3vQUdxWQXc3aYpNXl01cVKknn+eL6IdYuLfzUxNqfb2+v+o6wcTgxZjiTKIGnTcTvMNRc3I2cy+MJuC5iY03qkkPTicZ7EJ8s+3M4M67bbstOR5wDwGTPy7mU4WDFGNNqjZ6nGjilyUtY6bHSsorr9eqBpTJTeLJkrjDiBvi4CEDcz29QUPXg8NOSkkGn8vWQt6K6XLfPyNmPh3M83+bM3CHcMifJoJbNhxvRLm/omaF6jdgehOw0k9++IStDwO0BbJPWPC3zIm/t1MWM+/kyUKm4lVu7mAouwjSo++k0HlVlxZUe6M/F4XYYA2jjQXRShLliQ/+zynmPh3M0Ex5d4yhs3i6ZstURjFt9tOFtCwffT3x7dAfJR0EPX7b8dXHae9aZKWmyvrUobB9SYEiX8PRvEZHYX77oNqdWH92Wc7j5OTsHjxwve81ydOnL6/lSudv82hQ86+mx1369Zw+1auDN/H4CCION9BFWZmnMfDh2FsDGq18OO0emw4ff9HjrR+jNdfn7f3+PHWP58Vmzb5n/9uY3Iyawvyid8VIY/Fev4RaDWfT1x5+Jv1loNmwLazr6BedTN3UNQcO2HjH634/Pv7i9H7T3NgNqtUznkbXM8AzO1TAlr1b8eVyqFZTppWB9TCBMlvhaxGl1ej+EYRCe+C55eLpn5x9aiiUwRXQ5rhmF4bpi3+eVzMJmVM/MtAqz35OFM5NA4ct5sXJYogRYnLr+/xtbLPIB94q3MBihA7nrYYB602lnQpQURPM0z8y0A7Pfm08vBHJWpmyyguiyhZQOsFIkzYvd5rFPuK0PPPelH5tEqUPEpdTJD424BvNxE0sNtswGt01BnYPXUqfIA3LUTCt1EN32b9emcgNgrbtzuDv804cQIeewzuvx8GBoK36+8vxiBj1LYpOnNzWVuQS0z8u4nGyJnBQed1HgQ9Kps2OdEzQUS5MLTDiRNQqYRvd+SI057Hj8PU1OkRVtWqE3mV1zav1WDVquTaMYi0j1dPlPNaQrwFVnLP8PCwTk9PZ22GkTQ9PcG9+mo1+d7q4sXNwx8HB507pCIyMQH33hvtrilOlizJNgR2fBw2b87u+BkjIrtVdbix3nr+Rr5oFpe/ZIkjvknh3Sl5x2jsrYa50PLMunWwZUv6wg/w6qvpH9NjZKTUwt8ME38jXzQT18OH/cc1wPGzT02FXxwqFX9/vSfs3hiIKjzwQLFdaOC4eRYtgl27srMhS5/7zp3ZHTvnmPgb+SKKuPb4/Gxffx1uvRWuucb/4gCO22j79nl/fZiw53EwPCq1mnOntH49nDyZtTVGDjGfv5E/mg0ORvHJT046A8dHjjhuJK9HXxbWrcu2p58Xenos0odgn39vFsYYRlMqleA/bVguGi8ap0xiD3DppfDUU1lbkS/uuCNrC3JNYm4fEblbRJ4VkT1uuabuvd8SkYMickBEfikpG4yCMjbW/mfDErl1Iyb8pzM0ZAO9ISTt8/+8qq51y2MAIjIE3ARcClwNbBYRC8Q15tm82QnP8+KzKxXnddhgbl9fcaNxOsGEf56eHue3sm9f1pbkniwGfK8DHlLVk6r6A+AgcEUGdhh5ZvNmmJ11om5mZ53XQZE+4Azm3n9/+dw9ZWFoaOHrxolb1aoziD83Zz3+iCTt8/+EiHwUmAZ+U1X/FTgPeLxum6Nu3WmIyBgwBrCyjLfzxkI8YS/zYG5ZeeWVbOYodDEd9fxFZKeI7PUp1wFbgLcAa4Hngc96H/PZle9ZVdWtqjqsqsPLly/vxFSjWyhy+GXceKkaykA7i+wYTemo56+q66JsJyJ/AvyF+/IocEHd2+cDz3Vih2F0NY0DukNDsGJFucI5ly7N2oKuI8lonxV1L68H9rrPvw7cJCJniMhq4CLgiaTsMIxCMDEBvb3OHIfeXuc1+EfyPPVUuYTfSIQkff5/KCJrcVw6h4A7AFR1n4g8DDwFzAIfV1WbiWGUl0aBn5tz8vCARfJ4HDuWtQVdR2Lir6o3N3lvEihhTJ5hNLBuXbDAb92ari15xgI+Ysdy+xhGVtRqzd03lppgnjLO30gYE3/DSBMvQqenBzZsyNqaYjAwUO6oroSw3D6GkRaNCdesZx+OCHzxi1lb0ZWY+BtGkljenfYZGHCE33r9iWDibxhJYcLfOj098OUvm+CngPn8DSMpTPhbo1o14U8RE3/DiJuJieYL0hjzy26qzpeXXjLhTxFz+xhGnNgqWuFUq3DPPSb0GWPibxjtUKvBnXfCzIzzuqfHSTZnBDMw4KyfbOQCE3/DaJVazVkYvR4T/nAsZDNXmM/fMFrBT/iNcMbHzc2TM6znbxhRMF9+e5h/P7dYz98wwjDhb86SJU7kzuCgE+U0ODgfyWMRPLnFev6G0Yyw5Gtlp7cX7r3XEXgT+UJhPX/DCKJWg9tuy9qKfLNtm4l+QbGev2F41Gpw++3w2mtZW1IMpqZM+AuM9fyN7mFiwom3F5kv3nKIftSnV16yxIniMeGPjgl/obGev1FszjsPnnsu+H1vOcTNm50LwdatTiplEahUYHbWef+VV5K3tZsYGcnaAqNDEhN/EdkBvN19+Sbg31R1rYisAvYDB9z3HlfVjUnZYXQBjbNpW2XrVvjnf144cKs6L/xGa4yMwM6dWVthdEiSa/je6D0Xkc8CP6p7+2lVXZvUsY0uoL6X3ilzcxaxEwfVqhO6aXQFibt9RESAG4D/mPSxjALTae/eiEal0t4Ftb/fmaxldA1pDPi+E3hBVb9fV7daRP5JRP5BRN6Zgg1GnqnV4KMfNeGPC5HT0yV7ZXbWmYTVCtUq3HefDfB2GaKq7X9YZCfw0z5vbVLVr7nbbAEOqupn3ddnAEtUdUZELge+Clyqqj/22f8YMAawcuXKyw8fPty2rUaOWbbMhD9umv2vazUYG4MTJ4K3sbQMXYOI7FbV4cb6jtw+qrou5KC9wAeBy+s+cxI46T7fLSJPA28Dpn32vxXYCjA8PNz+VcrINyb88RLWs/cEfdMmOHIEVq6EyUkT+pKRtM9/HfA9VT3qVYjIcuCYqs6JyIXARcAzCdthGOVhcjJ8G0vHUHqSFv+bgAcb6t4F/K6IzAJzwEZVPZawHYZRHkzUjQgkKv6qeotP3Z8Df57kcQ2jtFQqWVtgFARL72Bkj80WjY+xsawtMAqCib+RPTt3wtBQ1lYUn/FxJ42FYUTAcvsY+WDfPicE8Y47LM9OO3QQsm2UE+v5G/lhdBSOH3cmKC1alLU1xWF8PGsLjAJi4m/kj9FRePVV5yJwxhlZW9MZIyOOOPck9Fc791xz9RhtYeJv5JfRUSe//tQUDAyc/r5I9H21sm2c7NzpiPPc3MI0C3GMcQwNwbPPdr4fo5SY+Bv5x3MHNeapeeCB+UXDq9XgMMfxcWcWa9o0c8fs27fwu7SSb2dgwLkg7tvXuY1GaTHxN4rL6CgcOgSnTjmphmdnHcH1LgKVynwEzOSks9h4GtQfNyqTk+Ex+tWqI/rHj9tELqNjLNrH6C42b/YXXU8sN250xDMuKhXYvr1zMfY+Xx/t1NPjvDafvpEAHWX1TJPh4WGdnj4t95thtEet5iQ26yRT7NCQuV6M3BOU1dPcPkY58VxGrc4u9lwvqib8RqEx8TfKzc6d0S4Anui/9JL5242uwHz+hmGLkRslxHr+hmEYJcTE3zAMo4SY+BuGYZQQE3/DMIwSYuJvGIZRQkz8DcMwSkhH4i8iHxaRfSJySkSGG977LRE5KCIHROSX6uovF5Hvuu/9N5Gs0i0ahmGUl057/nuBDwLfrK8UkSHgJuBS4Gpgs4h4Wau2AGPARW65ukMbDMMwjBbpSPxVdb+qHvB56zrgIVU9qao/AA4CV4jICuBsVf2WOkmFvgx8oBMbDMMwjNZJaobvecDjda+PunVvuM8b630RkTGcuwSA4yLid6GJwjLgpTY/myRmV2uYXa1hdrVGt9rlu1hEqPiLyE7gp33e2qSqXwv6mE+dNqn3RVW3AlvDbAxDRKb9stpljdnVGmZXa5hdrVE2u0LFX1XXtbHfo8AFda/PB55z68/3qTcMwzBSJKlQz68DN4nIGSKyGmdg9wlVfR54WUR+3o3y+SgQdPdgGIZhJESnoZ7Xi8hR4D8Afyki3wBQ1X3Aw8BTwF8BH1fVOfdj48CXcAaBnwb+Vyc2RKRj11FCmF2tYXa1htnVGqWyqzAreRmGYRjxYTN8DcMwSoiJv2EYRgnpKvEvSroJEdkhInvcckhE9rj1q0Tk1br37k3alga77haRZ+uOf03de77tl5JdfyQi3xORJ0XkURF5k1ufaXu5NlzttslBEbkr7ePX2XGBiPydiOx3/wN3uvWB5zRF2w65/7E9IjLt1i0Vkb8Rke+7j+ekbNPb69pkj4j8WER+LYv2EpH7RORFEdlbVxfYPrH9F1W1awpwCfB24O+B4br6IeA7wBnAapyB5or73hM4A9aCM/j8vpRt/izwO+7zVcDeDNvvbuCTPvWB7ZeSXe8Fet3nnwY+nZP2qrhtcSHQ77bRUEa2rAB+1n1+FvDP7nnzPacp23YIWNZQ94fAXe7zu7xzmuF5/H84k6FSby/gXcDP1v+Wg9onzv9iV/X8tWDpJty7jBuAB9M6Zpv4tl9aB1fVv1bVWffl4yycK5IlVwAHVfUZVX0deAinrVJHVZ9X1W+7z18G9tNk9nwOuA7Y7j7fTrZpXkaAp1X1cBYHV9VvAscaqoPaJ7b/YleJfxPOA/6l7rWXVuI8Wkg3kQDvBF5Q1e/X1a0WkX8SkX8QkXemaIvHJ1z3yn11t5pB7ZcFt7EwPDjL9spTu/wEEVkFvAP4P26V3zlNEwX+WkR2i5OyBeDN6sz7wX38qQzs8riJhR2wrNsLgtsntt9c4cRfRHaKyF6f0qzHFUu6iQTs/AgLf3TPAytV9R3AbwD/Q0TOjsOeiHZtAd4CrHVt+az3MZ9dxRojHKW9RGQTMAvU3KrE2yvMbJ+6TGOnRWQJ8OfAr6nqjwk+p2lypar+LPA+4OMi8q4MbPBFRPqB9wN/5lblob2aEdtvLqnEbomhBUk3EWaniPTipMO+vO4zJ4GT7vPdIvI08DZgOg6bothVZ9+fAH/hvgxqv9iI0F4bgGuBEddFl0p7hZB4u7SCiPThCH9NVR8BUNUX6t6vP6epoarPuY8visijOG6KF0Rkhao+77pfX0zbLpf3Ad/22ikP7eUS1D6x/eYK1/Nvkzymm1gHfE9Vf+J2EpHl4q57ICIXunY+k5I9uD8yj+tx1muAgPZL0a6rgf8MvF9VT9TVZ9pewP8FLhKR1W4P8iactkod9/f7p8B+Vf1cXX3QOU3LrgEROct7jjN4vxennTa4m20guzQvC+6+s26vOoLaJ77/YlYj7AmNml+Pc2U8CbwAfKPuvU04I+MHqIvoAYZxTvDTwBdwZz2nYOs2YGND3a8A+3BG878N/HLK7fcA8F3gSfdHtiKs/VKy6yCOn3OPW+7NQ3u5NlyDE1nzNE6m21SPX2fHL+Dc/j9Z107XNDunKdl1oXt+vuOeq01ufRXYBXzffVyaQZstBmaAf1dXl3p74Vx8nmc+5f3HmrVPXP9FS+9gGIZRQsri9jEMwzDqMPE3DMMoISb+hmEYJcTE3zAMo4SY+BuGYZQQE3/DMIwSYuJvGIZRQv4/uh1MbBEb8jMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tot_new[:4346, 0], tot_new[:4346, 1], color='r', label='positive')\n",
    "#plt.scatter(tot_new[4346:, 0], tot_new[4346:, 1], color='b', label='negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "7760e988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(tot_new[:4346, 0], tot_new[:4346, 1], color='r', label='positive')\n",
    "plt.scatter(tot_new[4346:, 0], tot_new[4346:, 1], color='b', label='negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10605a5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "707ca468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tot_new[:3482, 0], tot_new[:3482, 1], color='r', label='true positive')\n",
    "plt.scatter(tot_new[3482:4346, 0], tot_new[3482:4346, 1], color='g', label='false negative')\n",
    "plt.scatter(tot_new[4346:4979, 0], tot_new[4346:4979, 1], color='y', label='false negative')\n",
    "plt.scatter(tot_new[4979:, 0], tot_new[4979:, 1], color='b', label='true negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "d4e0b398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tot_new[:3482, 0], tot_new[:3482, 1], color='r', label='true positive')\n",
    "# plt.scatter(tot_new[3482:4346, 0], tot_new[3482:4346, 1], color='g', label='false negative')\n",
    "# plt.scatter(tot_new[4346:4979, 0], tot_new[4346:4979, 1], color='y', label='false negative')\n",
    "# plt.scatter(tot_new[4979:, 0], tot_new[4979:, 1], color='b', label='true negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "086006e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(tot_new[:3482, 0], tot_new[:3482, 1], color='r', label='true positive')\n",
    "plt.scatter(tot_new[3482:4346, 0], tot_new[3482:4346, 1], color='g', label='false negative')\n",
    "# plt.scatter(tot_new[4346:4979, 0], tot_new[4346:4979, 1], color='y', label='false negative')\n",
    "# plt.scatter(tot_new[4979:, 0], tot_new[4979:, 1], color='b', label='true negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "9281151f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(tot_new[:3482, 0], tot_new[:3482, 1], color='r', label='true positive')\n",
    "# plt.scatter(tot_new[3482:4346, 0], tot_new[3482:4346, 1], color='g', label='false negative')\n",
    "plt.scatter(tot_new[4346:4979, 0], tot_new[4346:4979, 1], color='y', label='false negative')\n",
    "# plt.scatter(tot_new[4979:, 0], tot_new[4979:, 1], color='b', label='true negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "95bb56cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(tot_new[:3482, 0], tot_new[:3482, 1], color='r', label='true positive')\n",
    "# plt.scatter(tot_new[3482:4346, 0], tot_new[3482:4346, 1], color='g', label='false negative')\n",
    "# plt.scatter(tot_new[4346:4979, 0], tot_new[4346:4979, 1], color='y', label='false negative')\n",
    "plt.scatter(tot_new[4979:, 0], tot_new[4979:, 1], color='b', label='true negative')\n",
    "plt.xlim(-110, 110)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e11fb1e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07207afb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
